Regression Using Decision Tree and Text Classification using Naive Bayes

In the previous report, I have commented about the use of decision tree in order to predict numerical values when there is multi-collinearity between predictors that render a linear regression model utterly obsolete. Here, I will try to use Decision Tree and evaluate it accordingly. In addition, I have also commented on the use of Naive Bayes to classify text because Naive Bayes models assume that all predictors are independent of each other, which makes it suitable for text classification.

1 Decision Tree

In this report, I will use dataset of the housing prices in Melbourne using decision tree and compare with linear regression model. My hypothesis currently would be that a decision tree model is better than a linear regression model, because this dataset have predictors that are both of numerical and factor data types, making decision tree to be more suitable than a linear regression model (that only uses numerical predictors). In addition, there may be multi-collinearity between the predictors in the dataset, such as the column 'rooms', 'bathrooms', and 'bedrooms', making a decision tree model better.

1.1 Read and pre-processing data

library(tidyverse)
library(lubridate)
house <- read_csv("Melbourne_housing.csv")

house <- house %>% 
  mutate_if(is.character, as.factor) %>% 
  mutate(Address = as.character(Address)) %>% 
  mutate(Date = dmy(Date))
house

anyNA(house)

## [1] TRUE

dim(house)

## [1] 34857    21

house %>% 
  is.na() %>% 
  colSums()

##        Suburb       Address         Rooms          Type         Price 
##             0             0             0             0          7610 
##        Method       SellerG          Date      Distance      Postcode 
##             0             0             0             1             1 
##      Bedroom2      Bathroom           Car      Landsize  BuildingArea 
##          8217          8226          8728         11810         21115 
##     YearBuilt   CouncilArea     Lattitude    Longtitude    Regionname 
##         19306             0          7976          7976             0 
## Propertycount 
##             3

From the data above, we can tell that there are a lot of missing values which we must either clear or replace. As a rule of thumb, because the number of NA is below 5% of the data, we can delete the rows on the missing data. Since Propertycount only has 3 missing values, Postcode only has 1 missing value and Distance only has 1 missing value, we will delete those row of missing observations.

house <- house %>% 
  filter(Propertycount != "NA") %>% 
  filter(Postcode != "NA") %>% 
  filter(Distance != "NA")

house %>% 
  is.na() %>% 
  colSums()

##        Suburb       Address         Rooms          Type         Price 
##             0             0             0             0          7610 
##        Method       SellerG          Date      Distance      Postcode 
##             0             0             0             0             0 
##      Bedroom2      Bathroom           Car      Landsize  BuildingArea 
##          8214          8223          8725         11807         21112 
##     YearBuilt   CouncilArea     Lattitude    Longtitude    Regionname 
##         19303             0          7973          7973             0 
## Propertycount 
##             0

dim(house)

## [1] 34854    21

Although there are a lot of missing values on some columns, I find those columns pretty influential on our predictor. Hence, instead of deleting the entire column, we will replace it with the mean of the data if the column consists of numerical values, or if it is a factor, we will replace it with the mode of the data in that column.

house_nona <- house %>% 
  drop_na()

house_clean <- house %>% 
  mutate(YearBuilt = replace_na(YearBuilt, median(house_nona$YearBuilt))) %>% 
  mutate(Lattitude = replace_na(Lattitude, median(house_nona$Lattitude))) %>% 
  mutate(Longtitude = replace_na(Longtitude, median(house_nona$Longtitude))) %>% 
  mutate(Bedroom2 = replace_na(Bedroom2, median(house_nona$Bedroom2))) %>% 
  mutate(Car = replace_na(Car, median(house_nona$Car))) %>% 
  mutate(Landsize = replace_na(Landsize, median(house_nona$Landsize))) %>% 
  mutate(BuildingArea = replace_na(BuildingArea, median(house_nona$BuildingArea))) %>% 
  mutate(Price = replace_na(Price, median(house_nona$Price))) %>% 
  mutate(Bathroom = replace_na(Bathroom, median(house_nona$Bathroom)))

In order to make our decision tree less messy, we will try to filter out predictors that have too many levels, or just insignificant to our prediction of price.

library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

ggcorr(house_clean, label = T)

## Warning in ggcorr(house_clean, label = T): data in column(s) 'Suburb',
## 'Address', 'Type', 'Method', 'SellerG', 'Date', 'CouncilArea', 'Regionname' are
## not numeric and were ignored

From the plot above, we will filter out columns that have no or very low correlation to price, namely Postcode, Landsize, Car, BuildingArea and Propertycount.

From columns that have factor data types, we will filter out mainly Type, Suburb (too many levels), Method, SellerG (too many levels) and CouncilArea (too many levels). I will also take out address and date.

str(house_clean)

## tibble [34,854 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ Suburb       : Factor w/ 351 levels "Abbotsford","Aberfeldie",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Address      : chr [1:34854] "68 Studley St" "85 Turner St" "25 Bloomburg St" "18/659 Victoria St" ...
##  $ Rooms        : num [1:34854] 2 2 2 3 3 3 4 4 2 2 ...
##  $ Type         : Factor w/ 3 levels "h","t","u": 1 1 1 3 1 1 1 1 1 1 ...
##  $ Price        : num [1:34854] 900000 1480000 1035000 900000 1465000 ...
##  $ Method       : Factor w/ 9 levels "PI","PN","S",..: 7 3 3 8 6 1 8 5 3 3 ...
##  $ SellerG      : Factor w/ 388 levels "@Realty","A",..: 171 34 34 313 34 34 246 246 34 76 ...
##  $ Date         : Date[1:34854], format: "2016-09-03" "2016-12-03" ...
##  $ Distance     : num [1:34854] 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 ...
##  $ Postcode     : num [1:34854] 3067 3067 3067 3067 3067 ...
##  $ Bedroom2     : num [1:34854] 2 2 2 3 3 3 3 3 4 3 ...
##  $ Bathroom     : num [1:34854] 1 1 1 2 2 2 1 2 1 2 ...
##  $ Car          : num [1:34854] 1 1 0 1 0 1 2 2 2 1 ...
##  $ Landsize     : num [1:34854] 126 202 156 0 134 94 120 400 201 202 ...
##  $ BuildingArea : num [1:34854] 132 132 79 132 150 132 142 220 132 132 ...
##  $ YearBuilt    : num [1:34854] 1970 1970 1900 1970 1900 ...
##  $ CouncilArea  : Factor w/ 34 levels "#N/A","Banyule City Council",..: 33 33 33 33 33 33 33 33 33 33 ...
##  $ Lattitude    : num [1:34854] -37.8 -37.8 -37.8 -37.8 -37.8 ...
##  $ Longtitude   : num [1:34854] 145 145 145 145 145 ...
##  $ Regionname   : Factor w/ 9 levels "#N/A","Eastern Metropolitan",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Propertycount: num [1:34854] 4019 4019 4019 4019 4019 ...
##  - attr(*, "problems")= tibble [5 × 5] (S3: tbl_df/tbl/data.frame)
##   ..$ row     : int [1:5] 18524 26889 29484 29484 29484
##   ..$ col     : chr [1:5] "Propertycount" "Propertycount" "Distance" "Postcode" ...
##   ..$ expected: chr [1:5] "a double" "a double" "a double" "a double" ...
##   ..$ actual  : chr [1:5] "#N/A" "#N/A" "#N/A" "#N/A" ...
##   ..$ file    : chr [1:5] "'Melbourne_housing.csv'" "'Melbourne_housing.csv'" "'Melbourne_housing.csv'" "'Melbourne_housing.csv'" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   Suburb = col_character(),
##   ..   Address = col_character(),
##   ..   Rooms = col_double(),
##   ..   Type = col_character(),
##   ..   Price = col_double(),
##   ..   Method = col_character(),
##   ..   SellerG = col_character(),
##   ..   Date = col_character(),
##   ..   Distance = col_double(),
##   ..   Postcode = col_double(),
##   ..   Bedroom2 = col_double(),
##   ..   Bathroom = col_double(),
##   ..   Car = col_double(),
##   ..   Landsize = col_double(),
##   ..   BuildingArea = col_double(),
##   ..   YearBuilt = col_double(),
##   ..   CouncilArea = col_character(),
##   ..   Lattitude = col_double(),
##   ..   Longtitude = col_double(),
##   ..   Regionname = col_character(),
##   ..   Propertycount = col_double()
##   .. )

house_new <- house_clean %>% 
  select(c(Rooms, Type, Distance, Bedroom2, Bathroom, Lattitude, Longtitude, Regionname, Price))

1.2 Cross Validation

library(rsample)
set.seed(100)
idx <- initial_split(data = house_new, strata = Price, prop = 0.8)
train <- training(idx)
test <- testing(idx)

1.3 Modelling, model evaluation and tuning

library(partykit)
house_tree <- ctree(formula = Price~., train)
plot(house_tree, type = "simple")

library(MLmetrics)
pred <- predict(house_tree, newdata = test)
RMSE(y_pred = pred, y_true = test$Price)

## [1] 446043.7

range(test$Price)[1]

## [1] 145000

1.4 Comparison with linear regression

house_lm <- house_clean %>% 
  select_if(is.numeric)

library(lmtest)
library(car)
set.seed(100)
idx1 <- initial_split(data = house_lm, strata = Price, prop = 0.8)
train1 <- training(idx1)
test1 <- testing(idx1)
model_all <- lm(Price~., data = train1)
backward <- step(model_all, direction = "backward", trace = F)
pred0 <- predict(backward, newdata = test1)
RMSE(y_pred = pred0, y_true = test1$Price)

## [1] 510510.6

range(test1$Price)[1]

## [1] 145000

vif(backward)

##         Rooms      Distance      Postcode      Bedroom2      Bathroom 
##      3.242642      1.538160      1.460787      3.858457      1.660817 
##           Car      Landsize  BuildingArea     YearBuilt     Lattitude 
##      1.213943      1.003476      1.107834      1.112520      1.162417 
##    Longtitude Propertycount 
##      1.246332      1.007178

According to the result of the linear regression model, multi-collinearity is not detected (unlike my hypothesis). However, with that being said, the decision tree model performs better than the linear regression model. Based on the root-mean-square, the RMSE of the decision tree model (428142.6) is less than the RMSE of the linear regression model. (469928.6)

In addition, since a decision tree model is highly susceptible to over-fitting, we will try to detect that problem.

pred2 <- predict(house_tree, newdata = train)
RMSE(y_pred = pred2, y_true = train$Price)

## [1] 415575.8

range(train$Price)[1]

## [1] 85000

From the above, we can see that using the train dataset, the RMSE is slightly lower than the RMSE of the test dataset. However, the lowest end of the range of price on the train dataset is lower than the lowest end of the range of price on the test dataset.

(((RMSE(y_pred = pred0, y_true = test1$Price) - range(test1$Price)[1]))/(range(test1$Price)[1]))*100

## [1] 252.0763

(((RMSE(y_pred = pred2, y_true = train$Price) -
range(train$Price)[1]))/(range(train$Price)[1]))*100

## [1] 388.9127

In terms of percentage, the RMSE of the test dataset is 224% higher than the lowest end of the range of price on the test dataset, whereas the RMSE of the train dataset is 392% higher than the lowest end of the range of price on the train dataset. Hence, we concluded that there is no problem of overfitting.

However, regardless, the decision tree model does not perform very well as the lowest end of the range of price on the test dataset is way less than the RMSE, making this model highly inaccurate and unreliable in predicting future.

1.5 Model Tuning and conclusion

We will try to tune it through changing the min-split, min-criterion, and min-bucket on the parameters of the decision tree model.

house_tree2 <- ctree(formula = Price~., 
                     data = train,
                     control = ctree_control(minbucket = 100,
                                             mincriterion = 0.95,
                                             minsplit = 100))
pred00 <- predict(house_tree2, newdata = test)
RMSE(y_pred = pred00, y_true = test$Price)

## [1] 441958.2

range(test$Price)[1]

## [1] 145000

I have tried to tweak the model a few times and I could not bring the RMSE down further than the initial decision tree model.

In conclusion, I would say that for this dataset, the decision tree model fares better than a linear regression model. However, it is very likely that decision tree is not the most suitable model for this dataset prediction due to the relatively large RMSE in comparison to the lowest range of the test dataset.

2 Text classification using Naive Bayes

In this report, I will try to classify a bunch of job postings using Naive Bayes.

2.1 Read data and pre-processing

library(stringr)
job <- read_csv("fake_job_postings.csv")
head(job)

Because we are only focusing the text from the column of description, we will remove other irrelevant columns. We will also select the column fraudulent because it will be our target. (0 for real, 1 for fake)

For simplicity sake, we will also remove rows that contain missing values.

job_clean <- job %>% 
  select(c(description, fraudulent)) %>% 
  mutate(fraudulent = as.factor(fraudulent)) %>% 
  drop_na() %>% 
  mutate(description = str_to_lower(description))

2.2 Corpus

library(tm)
description.corpus <- VCorpus(VectorSource(job_clean$description))

2.3 Removal of numbers, words and punctuations

description.corpus <- tm_map(description.corpus, FUN = removeNumbers)
library(stopwords)
description.corpus <- tm_map(description.corpus, removeWords, stopwords("english"))
description.corpus <- tm_map(description.corpus, removePunctuation)

2.4 Stemming

library(SnowballC)
description.corpus <- tm_map(description.corpus, stemDocument)

2.5 Removal of white space

description.corpus <- tm_map(description.corpus, stripWhitespace)

2.6 Tokenisation

description_dtm <- DocumentTermMatrix(description.corpus)
inspect(description_dtm)

## <<DocumentTermMatrix (documents: 17880, terms: 82246)>>
## Non-/sparse entries: 1427574/1469130906
## Sparsity           : 100%
## Maximal term length: 287
## Weighting          : term frequency (tf)
## Sample             :
##        Terms
## Docs    busi client compani custom develop manag product servic team work
##   10336    2      5       2     11       4     5       0      0    9    9
##   1328     0      0       5      3       1     3       2      2    9    7
##   14186    0      2       3      9       0    14       2      8    9   10
##   14239    0      0       4      2       1     3       1      1    9    7
##   215      0      0       5      3       1     3       2      2    9    7
##   4747     2      6       3      1       0     6       0      1    1    5
##   4779     3     18       9      3       6     6       0     12    6   12
##   5441     0      1       3      0       6     4       2      2    4    5
##   6576     2      2       0      0       2    26       6      6   12    0
##   7847     3     12       1      0       6     2       3      4    5    3

2.7 Cross Validation

We will split the data into train and test dataset with 75% and 25% proportion respectively.

set.seed(50)
idx <- sample(nrow(description_dtm), nrow(description_dtm)*0.75)
train <- description_dtm[idx, ]
test <- description_dtm[-idx, ]
inspect(train)

## <<DocumentTermMatrix (documents: 13410, terms: 82246)>>
## Non-/sparse entries: 1066798/1101852062
## Sparsity           : 100%
## Maximal term length: 287
## Weighting          : term frequency (tf)
## Sample             :
##        Terms
## Docs    busi compani custom develop experi manag product servic team work
##   10336    2       2     11       4      3     5       0      0    9    9
##   14186    0       3      9       0      1    14       2      8    9   10
##   14239    0       4      2       1      3     3       1      1    9    7
##   267      0       2      7       1      1     9       6      5   15    7
##   4747     2       3      1       0      0     6       0      1    1    5
##   4779     3       9      3       6      9     6       0     12    6   12
##   5441     0       3      0       6      0     4       2      2    4    5
##   6576     2       0      0       2      0    26       6      6   12    0
##   7847     3       1      0       6      3     2       3      4    5    3
##   9498     6       7      0       1      2     0       0      1    4   12

Next, we will split the target variabel from the job_clean dataset with the same index split previously.

train_target <- job_clean[idx, "fraudulent"]
test_target <- job_clean[-idx, "fraudulent"]

2.8 Terms chosen

We will only choose terms that appear in more than 1000 documents to lessen the number of predictors.

desc_freq <- findFreqTerms(description_dtm, 1000)
length(desc_freq)

## [1] 376

#Subset the column of train-test dataset with just using column which column names are in sms_freq.
train_freq <- train[, desc_freq]
test_freq <- test[,desc_freq]
inspect(train_freq)

## <<DocumentTermMatrix (documents: 13410, terms: 376)>>
## Non-/sparse entries: 542426/4499734
## Sparsity           : 89%
## Maximal term length: 12
## Weighting          : term frequency (tf)
## Sample             :
##        Terms
## Docs    busi compani custom develop experi manag product servic team work
##   10336    2       2     11       4      3     5       0      0    9    9
##   11343    5       1      4      10      0    16      25     11    8    0
##   13283    2       2      0       2      3     5       4      0    7    5
##   14132   10       0      0      14      4    10       3     10   11    3
##   14239    0       4      2       1      3     3       1      1    9    7
##   4779     3       9      3       6      9     6       0     12    6   12
##   5441     0       3      0       6      0     4       2      2    4    5
##   6576     2       0      0       2      0    26       6      6   12    0
##   6986     1       8      0       4      0    10       0     10    6   16
##   7847     3       1      0       6      3     2       3      4    5    3

2.9 Bernoulli Conversion

Before the modelling part, we need to change all elements of matrix with just 1 or 0 (1 if the corresponding term appears in the document, and 0 otherwise)

bernoulli_conv <- function(x){
  x <- as.factor(as.numeric(x > 0))
}

train_bn <- apply(train_freq, 2, bernoulli_conv)
test_bn <- apply(test_freq, 2, bernoulli_conv)
train_bn

##           Terms
##            ’ll ’re abil abl account accur achiev across activ addit administr
##       [1,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##       [2,] "0" "0" "1"  "0" "0"     "0"   "0"    "1"    "1"   "0"   "0"      
##       [3,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##       [4,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "1"   "0"      
##       [5,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##       [6,] "1" "1" "0"  "0" "0"     "0"   "0"    "1"    "0"   "0"   "0"      
##       [7,] "0" "0" "0"  "0" "1"     "0"   "0"    "0"    "0"   "0"   "0"      
##       [8,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##       [9,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "1"   "0"      
##      [10,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [11,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [12,] "0" "0" "0"  "1" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [13,] "0" "0" "0"  "0" "0"     "0"   "0"    "1"    "0"   "0"   "0"      
##      [14,] "0" "0" "0"  "0" "0"     "1"   "1"    "0"    "0"   "0"   "0"      
##      [15,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "1"      
##      [16,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [17,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [18,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [19,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "1"   "0"   "0"      
##      [20,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [21,] "0" "0" "0"  "0" "0"     "0"   "0"    "1"    "0"   "0"   "0"      
##      [22,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [23,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [24,] "1" "1" "0"  "1" "0"     "0"   "0"    "1"    "0"   "0"   "0"      
##      [25,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [26,] "1" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "1"      
##      [27,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [28,] "0" "0" "0"  "1" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [29,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [30,] "0" "1" "0"  "0" "1"     "0"   "0"    "0"    "1"   "0"   "0"      
##      [31,] "0" "0" "0"  "0" "1"     "0"   "1"    "1"    "0"   "0"   "0"      
##      [32,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [33,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [34,] "0" "0" "0"  "0" "0"     "0"   "1"    "0"    "1"   "0"   "0"      
##      [35,] "0" "0" "0"  "1" "1"     "0"   "0"    "1"    "1"   "1"   "0"      
##      [36,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "1"   "0"      
##      [37,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [38,] "0" "0" "0"  "0" "0"     "0"   "0"    "1"    "0"   "0"   "0"      
##      [39,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [40,] "0" "1" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [41,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [42,] "0" "0" "0"  "0" "1"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [43,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [44,] "0" "0" "0"  "1" "0"     "0"   "0"    "1"    "0"   "0"   "0"      
##      [45,] "0" "0" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
##      [46,] "1" "1" "0"  "0" "0"     "0"   "0"    "0"    "0"   "0"   "0"      
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##           Terms
##            advertis agenc also amp analysi analyt app appli applic appropri
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##           Terms
##            area around aspect assign assist associ autom avail back base believ
##       [1,] "0"  "0"    "0"    "0"    "1"    "0"    "0"   "0"   "0"  "0"  "0"   
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##           Terms
##            benefit best big brand bring build busi call campaign can candid
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##           Terms
##            care career center challeng chang client close code collabor come
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##           Terms
##            commit communic communiti compani competit complet complex comput
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##     [256,] "0"    "1"      "1"       "0"     "0"      "0"     "0"     "0"   
##     [257,] "0"    "0"      "0"       "0"     "0"      "0"     "0"     "0"   
##     [258,] "0"    "0"      "0"       "1"     "0"      "0"     "0"     "0"   
##     [259,] "0"    "0"      "0"       "1"     "0"      "0"     "0"     "0"   
##     [260,] "0"    "0"      "0"       "0"     "0"      "0"     "0"     "0"   
##     [261,] "0"    "0"      "0"       "1"     "0"      "0"     "0"     "0"   
##     [262,] "0"    "1"      "1"       "0"     "0"      "0"     "0"     "0"   
##     [263,] "0"    "0"      "0"       "0"     "0"      "0"     "0"     "0"   
##     [264,] "0"    "1"      "0"       "0"     "0"      "0"     "0"     "0"   
##     [265,] "0"    "1"      "0"       "0"     "0"      "0"     "0"     "0"   
##           Terms
##            consult consum contact content continu contract contribut control
##       [1,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##       [2,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##       [3,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##       [4,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##       [5,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##       [6,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##       [7,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##       [8,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##       [9,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##      [10,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [11,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [12,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [13,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##      [14,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [15,] "0"     "0"    "1"     "0"     "1"     "0"      "0"       "0"    
##      [16,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [17,] "0"     "0"    "0"     "0"     "0"     "0"      "1"       "0"    
##      [18,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [19,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [20,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##      [21,] "0"     "1"    "0"     "1"     "0"     "0"      "0"       "0"    
##      [22,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [23,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "1"    
##      [24,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [25,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [26,] "1"     "0"    "0"     "0"     "0"     "0"      "1"       "0"    
##      [27,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [28,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [29,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [30,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [31,] "0"     "1"    "1"     "0"     "0"     "0"      "0"       "0"    
##      [32,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [33,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##      [34,] "0"     "0"    "0"     "0"     "0"     "0"      "1"       "0"    
##      [35,] "0"     "0"    "0"     "0"     "1"     "0"      "1"       "0"    
##      [36,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [37,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [38,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [39,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [40,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [41,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##      [42,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [43,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [44,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [45,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [46,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [47,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##      [48,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [49,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##      [50,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [51,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [52,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##      [53,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [54,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [55,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [56,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [57,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [58,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "1"    
##      [59,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [60,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "1"    
##      [61,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##      [62,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [63,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [64,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [65,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [66,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [67,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [68,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [69,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##      [70,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##      [71,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [72,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [73,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##      [74,] "0"     "0"    "1"     "0"     "1"     "0"      "0"       "0"    
##      [75,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [76,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [77,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [78,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##      [79,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [80,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [81,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [82,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [83,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [84,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##      [85,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [86,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [87,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [88,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [89,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [90,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [91,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [92,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [93,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [94,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [95,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##      [96,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##      [97,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##      [98,] "0"     "0"    "1"     "0"     "1"     "0"      "1"       "0"    
##      [99,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [100,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##     [101,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [102,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##     [103,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [104,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [105,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [106,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##     [107,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [108,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##     [109,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [110,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [111,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "1"    
##     [112,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [113,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [114,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [115,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [116,] "0"     "1"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [117,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [118,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [119,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [120,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [121,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [122,] "0"     "0"    "1"     "0"     "1"     "0"      "0"       "0"    
##     [123,] "0"     "0"    "0"     "0"     "1"     "0"      "1"       "0"    
##     [124,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [125,] "1"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##     [126,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [127,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [128,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [129,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [130,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [131,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [132,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [133,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [134,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [135,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [136,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [137,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [138,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [139,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [140,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [141,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [142,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##     [143,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [144,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [145,] "0"     "0"    "0"     "0"     "0"     "0"      "1"       "0"    
##     [146,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [147,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [148,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [149,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [150,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [151,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [152,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [153,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##     [154,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [155,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##     [156,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [157,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [158,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
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##     [160,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [161,] "0"     "0"    "0"     "0"     "0"     "1"      "0"       "0"    
##     [162,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [163,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [164,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [165,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [166,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [167,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [168,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [169,] "0"     "0"    "1"     "0"     "0"     "1"      "0"       "0"    
##     [170,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [171,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [172,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [173,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##     [174,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "1"    
##     [175,] "0"     "0"    "0"     "0"     "0"     "0"      "1"       "0"    
##     [176,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [177,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [178,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [179,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
##     [180,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [181,] "0"     "0"    "0"     "0"     "0"     "0"      "1"       "0"    
##     [182,] "0"     "0"    "0"     "0"     "1"     "0"      "0"       "0"    
##     [183,] "0"     "0"    "0"     "1"     "0"     "0"      "0"       "0"    
##     [184,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [185,] "0"     "1"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [186,] "1"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [187,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [188,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [189,] "0"     "0"    "0"     "0"     "0"     "0"      "0"       "0"    
##     [190,] "0"     "0"    "1"     "0"     "0"     "0"      "0"       "0"    
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##           Terms
##            coordin corpor cost creat creativ cultur current custom daili data
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##      [30,] "1"     "0"    "0"  "0"   "1"     "0"    "0"     "1"    "0"   "0" 
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##      [35,] "0"     "0"    "0"  "1"   "1"     "1"    "0"     "1"    "1"   "0" 
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##      [38,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "1" 
##      [39,] "1"     "0"    "1"  "0"   "0"     "0"    "0"     "0"    "0"   "0" 
##      [40,] "0"     "0"    "0"  "1"   "0"     "0"    "0"     "0"    "0"   "0" 
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##      [63,] "0"     "0"    "0"  "1"   "0"     "0"    "0"     "0"    "1"   "0" 
##      [64,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
##      [65,] "0"     "0"    "0"  "1"   "0"     "0"    "0"     "0"    "0"   "0" 
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##      [72,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "0" 
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##      [76,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
##      [77,] "0"     "0"    "0"  "0"   "0"     "0"    "1"     "0"    "0"   "0" 
##      [78,] "0"     "0"    "0"  "0"   "0"     "0"    "1"     "1"    "0"   "0" 
##      [79,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "1" 
##      [80,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "1"   "0" 
##      [81,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
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##      [88,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "1"   "0" 
##      [89,] "0"     "0"    "0"  "0"   "0"     "0"    "1"     "0"    "0"   "1" 
##      [90,] "0"     "0"    "0"  "0"   "0"     "1"    "0"     "0"    "0"   "0" 
##      [91,] "0"     "0"    "0"  "0"   "0"     "1"    "0"     "0"    "1"   "0" 
##      [92,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
##      [93,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "1" 
##      [94,] "0"     "0"    "0"  "1"   "0"     "0"    "1"     "0"    "0"   "0" 
##      [95,] "0"     "0"    "0"  "1"   "0"     "0"    "1"     "1"    "0"   "0" 
##      [96,] "0"     "0"    "1"  "0"   "0"     "0"    "0"     "1"    "0"   "1" 
##      [97,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "0" 
##      [98,] "0"     "0"    "1"  "0"   "1"     "0"    "0"     "0"    "1"   "0" 
##      [99,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "0" 
##     [100,] "0"     "0"    "0"  "1"   "0"     "0"    "0"     "0"    "0"   "0" 
##     [101,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
##     [102,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
##     [103,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "0" 
##     [104,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "1" 
##     [105,] "0"     "0"    "0"  "0"   "0"     "0"    "0"     "0"    "0"   "0" 
##     [106,] "1"     "0"    "0"  "0"   "0"     "0"    "0"     "1"    "0"   "0" 
##     [107,] "0"     "0"    "0"  "1"   "0"     "0"    "0"     "0"    "0"   "0" 
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##           Terms
##            databas day defin degre deliv deliveri demonstr depart design detail
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##           Terms
##            develop differ digit direct director document drive duti dynam educ
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##     [240,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [241,] "0"     "1"    "0"   "0"    "0"      "0"      "0"   "0"  "1"   "0" 
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##     [245,] "1"     "0"    "0"   "1"    "0"      "0"      "1"   "1"  "0"   "0" 
##     [246,] "1"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [247,] "1"     "0"    "1"   "1"    "1"      "0"      "1"   "0"  "1"   "0" 
##     [248,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [249,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
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##     [253,] "1"     "0"    "0"   "1"    "0"      "0"      "0"   "0"  "0"   "1" 
##     [254,] "1"     "0"    "0"   "0"    "0"      "0"      "1"   "0"  "0"   "0" 
##     [255,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [256,] "0"     "0"    "1"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [257,] "0"     "0"    "0"   "1"    "0"      "0"      "0"   "0"  "1"   "0" 
##     [258,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [259,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [260,] "1"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [261,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [262,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [263,] "0"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [264,] "1"     "0"    "0"   "0"    "0"      "0"      "0"   "0"  "0"   "0" 
##     [265,] "0"     "0"    "0"   "0"    "1"      "1"      "0"   "1"  "0"   "0" 
##           Terms
##            effect effici email employ employe end engag engin ensur environ
##       [1,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "0"   "0"    
##       [2,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##       [3,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##       [4,] "0"    "0"    "0"   "0"    "0"     "0" "1"   "1"   "0"   "0"    
##       [5,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##       [6,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##       [7,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##       [8,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##       [9,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "1"   "1"    
##      [10,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [11,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [12,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [13,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [14,] "1"    "1"    "0"   "0"    "1"     "0" "0"   "0"   "1"   "0"    
##      [15,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [16,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [17,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [18,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [19,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [20,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [21,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [22,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [23,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "0"   "0"    
##      [24,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [25,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [26,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [27,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [28,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [29,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "1"   "0"    
##      [30,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##      [31,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "1"    
##      [32,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [33,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [34,] "1"    "0"    "0"   "0"    "0"     "0" "1"   "0"   "1"   "1"    
##      [35,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "1"    
##      [36,] "0"    "0"    "0"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##      [37,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [38,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [39,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [40,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##      [41,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [42,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "0"   "0"    
##      [43,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [44,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##      [45,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "0"   "0"    
##      [46,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##      [47,] "0"    "0"    "0"   "0"    "0"     "0" "1"   "0"   "0"   "0"    
##      [48,] "0"    "1"    "1"   "0"    "0"     "0" "1"   "0"   "0"   "0"    
##      [49,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [50,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [51,] "0"    "0"    "0"   "0"    "0"     "0" "1"   "0"   "0"   "0"    
##      [52,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [53,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##      [54,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [55,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [56,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [57,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [58,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [59,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [60,] "0"    "0"    "0"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##      [61,] "0"    "0"    "1"   "1"    "1"     "0" "0"   "1"   "1"   "0"    
##      [62,] "0"    "0"    "0"   "0"    "0"     "1" "0"   "0"   "0"   "0"    
##      [63,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [64,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [65,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "1"    
##      [66,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##      [67,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [68,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [69,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [70,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##      [71,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [72,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "1"   "0"    
##      [73,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##      [74,] "0"    "0"    "0"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##      [75,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [76,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [77,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [78,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [79,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [80,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##      [81,] "1"    "0"    "0"   "0"    "0"     "1" "0"   "0"   "1"   "0"    
##      [82,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [83,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [84,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [85,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [86,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [87,] "0"    "1"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [88,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [89,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "1"    
##      [90,] "0"    "1"    "0"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##      [91,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [92,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "1"    
##      [93,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [94,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##      [95,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [96,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [97,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##      [98,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##      [99,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [100,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [101,] "0"    "0"    "0"   "0"    "0"     "0" "1"   "0"   "0"   "0"    
##     [102,] "1"    "0"    "1"   "1"    "1"     "0" "0"   "0"   "0"   "0"    
##     [103,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [104,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [105,] "0"    "0"    "0"   "0"    "0"     "1" "0"   "0"   "0"   "0"    
##     [106,] "1"    "0"    "1"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [107,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [108,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [109,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [110,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [111,] "1"    "0"    "0"   "0"    "0"     "0" "1"   "0"   "1"   "1"    
##     [112,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [113,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [114,] "0"    "0"    "1"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##     [115,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [116,] "0"    "0"    "1"   "1"    "1"     "0" "0"   "0"   "1"   "0"    
##     [117,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [118,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [119,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "1"    
##     [120,] "1"    "1"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [121,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [122,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [123,] "1"    "0"    "1"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##     [124,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [125,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [126,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [127,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [128,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [129,] "0"    "1"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [130,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [131,] "0"    "0"    "1"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [132,] "0"    "1"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [133,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [134,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [135,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "1"    
##     [136,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [137,] "0"    "1"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [138,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [139,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [140,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [141,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [142,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [143,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [144,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [145,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [146,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [147,] "1"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [148,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "1"   "0"    
##     [149,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "1"   "1"    
##     [150,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [151,] "0"    "0"    "0"   "0"    "1"     "0" "0"   "0"   "0"   "0"    
##     [152,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [153,] "0"    "1"    "0"   "0"    "0"     "0" "1"   "1"   "1"   "0"    
##     [154,] "0"    "1"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "1"    
##     [155,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [156,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [157,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [158,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [159,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [160,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "1"    
##     [161,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [162,] "0"    "1"    "0"   "0"    "1"     "0" "1"   "0"   "0"   "0"    
##     [163,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [164,] "1"    "0"    "0"   "1"    "0"     "0" "0"   "0"   "0"   "0"    
##     [165,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [166,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
##     [167,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [168,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [169,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [170,] "0"    "1"    "0"   "0"    "0"     "0" "0"   "0"   "0"   "0"    
##     [171,] "0"    "0"    "0"   "0"    "0"     "0" "0"   "1"   "0"   "0"    
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##           Terms
##            equip establish evalu event everi excel except excit execut exist
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##           Terms
##            expand expect experi experienc expert extern fast featur feel field
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##           Terms
##            file financ financi find firm first flexibl focus follow free friend
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##     [203,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [204,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "1"   "0"    "0"  "0"   
##     [205,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [206,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "1"  "0"   
##     [207,] "0"  "0"    "1"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [208,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [209,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "1"  "0"   
##     [210,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "1"   "0"    "0"  "0"   
##     [211,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [212,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [213,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [214,] "0"  "0"    "0"     "0"  "1"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [215,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [216,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [217,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [218,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [219,] "0"  "0"    "0"     "1"  "0"  "0"   "0"     "0"   "0"    "1"  "0"   
##     [220,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [221,] "0"  "0"    "1"     "0"  "0"  "1"   "0"     "0"   "0"    "0"  "0"   
##     [222,] "0"  "1"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "1"   
##     [223,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [224,] "1"  "0"    "0"     "0"  "0"  "1"   "0"     "0"   "0"    "0"  "0"   
##     [225,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [226,] "0"  "0"    "0"     "0"  "1"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [227,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "1"    "0"  "0"   
##     [228,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [229,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "1"  "0"   
##     [230,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "1"    "0"  "0"   
##     [231,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [232,] "0"  "0"    "0"     "1"  "0"  "0"   "0"     "1"   "1"    "0"  "0"   
##     [233,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [234,] "0"  "0"    "0"     "1"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [235,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [236,] "0"  "0"    "0"     "0"  "0"  "0"   "1"     "1"   "0"    "0"  "0"   
##     [237,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [238,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [239,] "0"  "0"    "0"     "0"  "0"  "0"   "1"     "0"   "0"    "1"  "0"   
##     [240,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "1"    "0"  "0"   
##     [241,] "0"  "0"    "1"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [242,] "0"  "0"    "1"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [243,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [244,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [245,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "1"    "0"  "0"   
##     [246,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [247,] "0"  "0"    "0"     "0"  "0"  "1"   "1"     "0"   "0"    "0"  "0"   
##     [248,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "1"   
##     [249,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [250,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "1"   "0"    "0"  "0"   
##     [251,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [252,] "0"  "0"    "0"     "0"  "0"  "1"   "0"     "0"   "0"    "0"  "0"   
##     [253,] "1"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "1"    "0"  "0"   
##     [254,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [255,] "0"  "0"    "0"     "1"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [256,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [257,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [258,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "1"  "0"   
##     [259,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [260,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [261,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "1"    "0"  "0"   
##     [262,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [263,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [264,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##     [265,] "0"  "0"    "0"     "0"  "0"  "0"   "0"     "0"   "0"    "0"  "0"   
##           Terms
##            full fulltim fun function fund futur general generat get global goal
##       [1,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##       [2,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "1" 
##       [3,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##       [4,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "1"    "0" 
##       [5,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##       [6,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "1" "0"    "1" 
##       [7,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##       [8,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##       [9,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [10,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [11,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [12,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [13,] "0"  "0"     "1" "0"      "1"  "1"   "0"     "0"     "0" "0"    "0" 
##      [14,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [15,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [16,] "0"  "0"     "0" "0"      "1"  "0"   "0"     "0"     "0" "0"    "0" 
##      [17,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [18,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "1"     "1" "0"    "0" 
##      [19,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [20,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [21,] "0"  "0"     "0" "0"      "1"  "0"   "0"     "0"     "0" "0"    "0" 
##      [22,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [23,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [24,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [25,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [26,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [27,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [28,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [29,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [30,] "1"  "0"     "0" "0"      "0"  "1"   "0"     "0"     "0" "0"    "0" 
##      [31,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "1" 
##      [32,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [33,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [34,] "0"  "0"     "0" "0"      "0"  "1"   "1"     "1"     "0" "1"    "0" 
##      [35,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [36,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [37,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [38,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [39,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [40,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [41,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "1"     "0" "0"    "0" 
##      [42,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [43,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [44,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "1"    "0" 
##      [45,] "1"  "0"     "1" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [46,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [47,] "1"  "0"     "1" "0"      "0"  "1"   "0"     "0"     "0" "0"    "1" 
##      [48,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [49,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [50,] "1"  "0"     "0" "0"      "1"  "0"   "0"     "0"     "0" "0"    "0" 
##      [51,] "0"  "0"     "0" "0"      "1"  "0"   "0"     "0"     "0" "0"    "1" 
##      [52,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [53,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [54,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [55,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [56,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [57,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [58,] "0"  "0"     "0" "1"      "0"  "1"   "0"     "0"     "0" "0"    "0" 
##      [59,] "0"  "0"     "0" "0"      "0"  "1"   "0"     "0"     "0" "0"    "0" 
##      [60,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [61,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [62,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [63,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [64,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [65,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [66,] "0"  "0"     "1" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [67,] "0"  "1"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [68,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [69,] "0"  "0"     "0" "0"      "1"  "1"   "0"     "0"     "0" "0"    "0" 
##      [70,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [71,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [72,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "1"    "0" 
##      [73,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [74,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [75,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [76,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [77,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [78,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [79,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [80,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [81,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "1"     "0" "0"    "0" 
##      [82,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [83,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [84,] "0"  "0"     "1" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [85,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [86,] "0"  "0"     "0" "0"      "0"  "0"   "1"     "0"     "0" "0"    "0" 
##      [87,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [88,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [89,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [90,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "1" "0"    "0" 
##      [91,] "1"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [92,] "1"  "0"     "0" "0"      "1"  "0"   "0"     "0"     "0" "0"    "0" 
##      [93,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [94,] "0"  "0"     "0" "0"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
##      [95,] "0"  "0"     "0" "1"      "0"  "0"   "0"     "0"     "0" "0"    "0" 
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##           Terms
##            good great group grow growth handl health help high highest hire
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##           Terms
##            home hour idea ideal identifi impact implement improv includ increas
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##           Terms
##            independ individu industri inform initi innov integr interact
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##     [265,] "1"      "0"      "0"      "1"    "0"   "0"   "0"    "0"     
##           Terms
##            interest intern interview involv issu job join just keep key know
##       [1,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "1"  "0"  "0" "0" 
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##      [45,] "0"      "1"    "0"       "0"    "0"  "0" "0"  "1"  "0"  "0" "0" 
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##      [51,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "1"  "0" "0" 
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##     [233,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [234,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "1"  "0" "0" 
##     [235,] "0"      "1"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [236,] "0"      "0"    "0"       "1"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [237,] "0"      "0"    "0"       "0"    "0"  "0" "1"  "0"  "0"  "0" "0" 
##     [238,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [239,] "1"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [240,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [241,] "0"      "1"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [242,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [243,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [244,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [245,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [246,] "0"      "0"    "0"       "0"    "0"  "0" "1"  "0"  "0"  "0" "0" 
##     [247,] "0"      "1"    "0"       "0"    "0"  "0" "1"  "0"  "0"  "0" "0" 
##     [248,] "1"      "0"    "0"       "1"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [249,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [250,] "0"      "0"    "0"       "1"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [251,] "1"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [252,] "0"      "1"    "0"       "0"    "0"  "0" "0"  "0"  "1"  "0" "0" 
##     [253,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [254,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [255,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [256,] "0"      "0"    "0"       "1"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [257,] "1"      "0"    "0"       "1"    "0"  "0" "1"  "0"  "0"  "0" "0" 
##     [258,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [259,] "0"      "0"    "0"       "0"    "0"  "0" "0"  "0"  "0"  "0" "0" 
##     [260,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [261,] "0"      "0"    "0"       "0"    "1"  "0" "0"  "0"  "0"  "0" "0" 
##     [262,] "0"      "1"    "0"       "1"    "0"  "0" "0"  "0"  "1"  "0" "0" 
##     [263,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##     [264,] "0"      "0"    "0"       "1"    "0"  "0" "0"  "0"  "1"  "0" "0" 
##     [265,] "0"      "0"    "0"       "0"    "0"  "1" "0"  "0"  "0"  "0" "0" 
##           Terms
##            knowledg larg lead leader leadership learn level life like limit
##       [1,] "0"      "0"  "0"  "0"    "0"        "0"   "0"   "0"  "0"  "0"  
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##      [29,] "0"      "0"  "1"  "0"    "0"        "0"   "0"   "0"  "0"  "0"  
##      [30,] "1"      "0"  "1"  "0"    "0"        "0"   "0"   "0"  "0"  "0"  
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##           Terms
##            live local locat look love mail main maintain mainten make manag
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##           Terms
##            manufactur market materi may media meet member mobil model monitor
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##           Terms
##            month motiv multipl must need network new next now object offer
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##     [257,] "0"   "1"   "0"     "1"  "0"  "1"     "0" "0"  "0" "0"    "0"  
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##     [265,] "0"   "0"   "0"     "0"  "0"  "0"     "0" "0"  "0" "0"    "0"  
##           Terms
##            offic one onlin open oper opportun optim order organ packag paid
##       [1,] "0"   "0" "0"   "1"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
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##       [3,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
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##       [9,] "1"   "1" "0"   "0"  "1"  "0"      "0"   "0"   "0"   "0"    "0" 
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##      [12,] "1"   "0" "0"   "0"  "1"  "1"      "0"   "1"   "1"   "0"    "0" 
##      [13,] "0"   "1" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
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##      [17,] "0"   "0" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [18,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "1"   "0"   "0"    "0" 
##      [19,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [20,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "1" 
##      [21,] "1"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [22,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [23,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [24,] "1"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [25,] "0"   "1" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [26,] "0"   "0" "0"   "0"  "1"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [27,] "0"   "0" "0"   "0"  "1"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [28,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [29,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [30,] "0"   "0" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "1"    "0" 
##      [31,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [32,] "0"   "1" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [33,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "1" 
##      [34,] "0"   "0" "0"   "1"  "1"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [35,] "1"   "0" "1"   "1"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [36,] "0"   "0" "0"   "1"  "1"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [37,] "0"   "0" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [38,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [39,] "0"   "0" "0"   "0"  "1"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [40,] "0"   "1" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [41,] "0"   "0" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [42,] "0"   "0" "0"   "1"  "1"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [43,] "0"   "0" "0"   "1"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [44,] "0"   "1" "0"   "0"  "0"  "1"      "0"   "0"   "0"   "0"    "0" 
##      [45,] "0"   "1" "0"   "0"  "1"  "0"      "0"   "0"   "0"   "0"    "1" 
##      [46,] "1"   "1" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [47,] "0"   "0" "0"   "0"  "0"  "1"      "0"   "0"   "1"   "0"    "0" 
##      [48,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "0" 
##      [49,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "0"   "0"   "0"    "1" 
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##     [265,] "0"   "0" "0"   "0"  "0"  "0"      "0"   "1"   "0"   "0"    "0" 
##           Terms
##            part particip partner passion patient peopl per perform person phone
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##           Terms
##            place plan platform play pleas polici posit potenti practic prepar
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##           Terms
##            present problem procedur process product profession program project
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##     [110,] "0"     "1"     "0"      "1"     "1"     "0"        "0"     "1"    
##     [111,] "0"     "0"     "1"      "0"     "0"     "0"        "0"     "1"    
##     [112,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [113,] "0"     "0"     "1"      "0"     "1"     "0"        "0"     "0"    
##     [114,] "0"     "0"     "0"      "1"     "1"     "0"        "0"     "0"    
##     [115,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [116,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "1"    
##     [117,] "1"     "0"     "0"      "0"     "1"     "1"        "1"     "0"    
##     [118,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [119,] "0"     "1"     "0"      "0"     "0"     "0"        "1"     "0"    
##     [120,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [121,] "0"     "0"     "0"      "0"     "1"     "0"        "1"     "0"    
##     [122,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "1"    
##     [123,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [124,] "0"     "0"     "0"      "1"     "0"     "0"        "1"     "1"    
##     [125,] "1"     "1"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [126,] "0"     "1"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [127,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [128,] "1"     "0"     "0"      "0"     "1"     "0"        "0"     "1"    
##     [129,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "1"    
##     [130,] "0"     "0"     "1"      "0"     "1"     "1"        "0"     "0"    
##     [131,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [132,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [133,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [134,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [135,] "1"     "0"     "0"      "0"     "0"     "1"        "0"     "1"    
##     [136,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [137,] "1"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [138,] "0"     "1"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [139,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [140,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [141,] "0"     "0"     "1"      "0"     "0"     "0"        "0"     "1"    
##     [142,] "0"     "0"     "0"      "1"     "1"     "0"        "0"     "0"    
##     [143,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [144,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [145,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [146,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [147,] "1"     "1"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [148,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [149,] "0"     "0"     "0"      "1"     "0"     "1"        "0"     "0"    
##     [150,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [151,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [152,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [153,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [154,] "1"     "1"     "0"      "1"     "0"     "0"        "0"     "1"    
##     [155,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [156,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [157,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [158,] "0"     "0"     "0"      "0"     "1"     "1"        "0"     "0"    
##     [159,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [160,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [161,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [162,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [163,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [164,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [165,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [166,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [167,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [168,] "1"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [169,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [170,] "0"     "1"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [171,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [172,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [173,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [174,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [175,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [176,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [177,] "0"     "0"     "1"      "0"     "1"     "0"        "0"     "0"    
##     [178,] "0"     "0"     "0"      "0"     "0"     "1"        "1"     "0"    
##     [179,] "1"     "0"     "0"      "0"     "1"     "0"        "1"     "0"    
##     [180,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [181,] "0"     "0"     "0"      "1"     "1"     "0"        "1"     "0"    
##     [182,] "0"     "0"     "0"      "1"     "1"     "0"        "0"     "1"    
##     [183,] "0"     "0"     "0"      "0"     "0"     "0"        "1"     "0"    
##     [184,] "0"     "0"     "0"      "1"     "1"     "0"        "0"     "1"    
##     [185,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [186,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [187,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [188,] "0"     "0"     "1"      "1"     "1"     "0"        "0"     "0"    
##     [189,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [190,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [191,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [192,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [193,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [194,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [195,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [196,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [197,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "1"    
##     [198,] "1"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [199,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [200,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [201,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [202,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [203,] "0"     "1"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [204,] "0"     "0"     "1"      "0"     "1"     "1"        "0"     "0"    
##     [205,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [206,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [207,] "0"     "0"     "0"      "0"     "1"     "1"        "0"     "0"    
##     [208,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [209,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [210,] "0"     "0"     "1"      "0"     "0"     "0"        "1"     "1"    
##     [211,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [212,] "0"     "0"     "1"      "0"     "0"     "0"        "0"     "0"    
##     [213,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [214,] "0"     "0"     "0"      "1"     "0"     "1"        "0"     "0"    
##     [215,] "0"     "0"     "0"      "1"     "1"     "0"        "0"     "0"    
##     [216,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [217,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [218,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [219,] "1"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [220,] "0"     "0"     "1"      "0"     "0"     "1"        "0"     "0"    
##     [221,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [222,] "0"     "0"     "0"      "1"     "1"     "0"        "1"     "0"    
##     [223,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [224,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [225,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [226,] "0"     "1"     "0"      "1"     "1"     "1"        "0"     "0"    
##     [227,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [228,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [229,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [230,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [231,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [232,] "1"     "1"     "0"      "1"     "1"     "0"        "0"     "0"    
##     [233,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [234,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [235,] "0"     "0"     "0"      "1"     "1"     "0"        "0"     "0"    
##     [236,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [237,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [238,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [239,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [240,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [241,] "0"     "0"     "0"      "1"     "1"     "0"        "1"     "0"    
##     [242,] "0"     "0"     "0"      "1"     "1"     "1"        "0"     "0"    
##     [243,] "0"     "0"     "0"      "0"     "0"     "0"        "1"     "1"    
##     [244,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [245,] "0"     "0"     "0"      "0"     "1"     "1"        "0"     "0"    
##     [246,] "0"     "1"     "0"      "0"     "1"     "1"        "0"     "0"    
##     [247,] "1"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [248,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [249,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [250,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "1"    
##     [251,] "0"     "1"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [252,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [253,] "0"     "0"     "1"      "0"     "0"     "1"        "1"     "0"    
##     [254,] "0"     "1"     "0"      "0"     "1"     "1"        "0"     "0"    
##     [255,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [256,] "0"     "0"     "0"      "0"     "0"     "1"        "0"     "0"    
##     [257,] "1"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [258,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [259,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [260,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "1"    
##     [261,] "0"     "0"     "0"      "1"     "0"     "0"        "0"     "0"    
##     [262,] "0"     "0"     "0"      "0"     "1"     "0"        "0"     "0"    
##     [263,] "0"     "0"     "0"      "0"     "0"     "0"        "0"     "0"    
##     [264,] "1"     "0"     "0"      "0"     "1"     "0"        "0"     "1"    
##     [265,] "0"     "1"     "0"      "0"     "0"     "0"        "1"     "0"    
##           Terms
##            promot prospect provid qualiti quick rang rapid real recommend
##       [1,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [2,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [3,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [4,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [5,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [6,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "1"  "0"      
##       [7,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [8,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##       [9,] "0"    "0"      "1"    "1"     "0"   "0"  "0"   "0"  "0"      
##      [10,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [11,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [12,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "1"  "0"      
##      [13,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [14,] "1"    "1"      "1"    "1"     "0"   "1"  "0"   "0"  "0"      
##      [15,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [16,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [17,] "0"    "0"      "0"    "1"     "0"   "0"  "0"   "0"  "0"      
##      [18,] "1"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [19,] "1"    "0"      "1"    "1"     "0"   "0"  "0"   "1"  "0"      
##      [20,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [21,] "0"    "0"      "0"    "0"     "1"   "0"  "1"   "0"  "0"      
##      [22,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [23,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [24,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [25,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [26,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [27,] "0"    "0"      "0"    "1"     "0"   "0"  "0"   "0"  "0"      
##      [28,] "0"    "0"      "0"    "0"     "0"   "1"  "0"   "0"  "0"      
##      [29,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [30,] "0"    "1"      "1"    "1"     "1"   "0"  "0"   "0"  "0"      
##      [31,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [32,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [33,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [34,] "0"    "0"      "1"    "1"     "0"   "0"  "0"   "0"  "0"      
##      [35,] "0"    "0"      "1"    "0"     "0"   "0"  "0"   "0"  "1"      
##      [36,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [37,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [38,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [39,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
##      [40,] "0"    "0"      "0"    "0"     "0"   "0"  "0"   "0"  "0"      
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##           Terms
##            record recruit relat relationship relev report repres request requir
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##           Terms
##            research resourc respons result resum retail revenu review right
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##           Terms
##            role run safeti sale schedul school search secur seek sell senior
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##           Terms
##            serv servic set share site skill small social softwar solut solv
##       [1,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
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##      [13,] "1"  "1"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
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##      [18,] "1"  "1"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##      [19,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##      [20,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
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##      [25,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##      [26,] "1"  "1"    "0" "0"   "0"  "1"   "1"   "0"    "0"     "1"   "0" 
##      [27,] "0"  "1"    "0" "0"   "0"  "1"   "0"   "0"    "1"     "0"   "0" 
##      [28,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##      [29,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "1"     "0"   "0" 
##      [30,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "1"   "0" 
##      [31,] "1"  "1"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##      [32,] "0"  "0"    "0" "1"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
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##     [197,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [198,] "0"  "1"    "1" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [199,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [200,] "0"  "0"    "0" "0"   "1"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [201,] "0"  "0"    "0" "0"   "0"  "1"   "1"   "0"    "0"     "0"   "0" 
##     [202,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [203,] "0"  "0"    "1" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [204,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [205,] "0"  "0"    "0" "0"   "1"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [206,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "0" 
##     [207,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "0" 
##     [208,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [209,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [210,] "1"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [211,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "0" 
##     [212,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [213,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##     [214,] "1"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "1"     "1"   "0" 
##     [215,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [216,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [217,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [218,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [219,] "0"  "1"    "1" "0"   "0"  "0"   "1"   "0"    "0"     "0"   "0" 
##     [220,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [221,] "1"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [222,] "0"  "0"    "1" "0"   "1"  "0"   "0"   "1"    "0"     "0"   "0" 
##     [223,] "0"  "0"    "0" "1"   "0"  "1"   "0"   "0"    "1"     "1"   "0" 
##     [224,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [225,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##     [226,] "0"  "1"    "1" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "1" 
##     [227,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [228,] "0"  "1"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [229,] "0"  "0"    "0" "0"   "1"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [230,] "1"  "1"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##     [231,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [232,] "0"  "1"    "0" "1"   "0"  "0"   "0"   "0"    "0"     "1"   "1" 
##     [233,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [234,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "1"    "0"     "0"   "0" 
##     [235,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [236,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "0" 
##     [237,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "1"     "0"   "0" 
##     [238,] "1"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [239,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [240,] "0"  "1"    "0" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [241,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [242,] "0"  "1"    "0" "0"   "1"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [243,] "1"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [244,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [245,] "0"  "1"    "1" "0"   "0"  "1"   "0"   "0"    "0"     "0"   "0" 
##     [246,] "0"  "0"    "0" "0"   "0"  "0"   "1"   "0"    "0"     "0"   "0" 
##     [247,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "0" 
##     [248,] "0"  "0"    "0" "0"   "1"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [249,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [250,] "0"  "0"    "0" "1"   "0"  "0"   "0"   "0"    "1"     "0"   "0" 
##     [251,] "0"  "0"    "0" "1"   "0"  "0"   "0"   "0"    "0"     "0"   "1" 
##     [252,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##     [253,] "0"  "1"    "1" "0"   "0"  "0"   "0"   "1"    "0"     "0"   "0" 
##     [254,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "1"     "0"   "0" 
##     [255,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [256,] "0"  "0"    "0" "0"   "0"  "1"   "0"   "1"    "0"     "0"   "0" 
##     [257,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "1"   "0" 
##     [258,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [259,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [260,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [261,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "1"     "0"   "0" 
##     [262,] "0"  "0"    "0" "0"   "0"  "1"   "1"   "1"    "0"     "0"   "0" 
##     [263,] "0"  "0"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##     [264,] "0"  "0"    "0" "0"   "0"  "0"   "1"   "0"    "0"     "0"   "0" 
##     [265,] "0"  "1"    "0" "0"   "0"  "0"   "0"   "0"    "0"     "0"   "0" 
##           Terms
##            someon sourc special specif staff standard start startup state
##       [1,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##       [2,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "1"     "0"  
##       [3,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##       [4,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##       [5,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##       [6,] "0"    "0"   "0"     "1"    "0"   "0"      "0"   "1"     "0"  
##       [7,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##       [8,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##       [9,] "0"    "0"   "0"     "0"    "1"   "1"      "0"   "0"     "0"  
##      [10,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [11,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [12,] "0"    "0"   "0"     "1"    "0"   "0"      "0"   "0"     "0"  
##      [13,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [14,] "0"    "0"   "0"     "0"    "1"   "1"      "0"   "0"     "0"  
##      [15,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [16,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [17,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [18,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [19,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [20,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [21,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [22,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [23,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [24,] "1"    "0"   "0"     "0"    "1"   "0"      "1"   "0"     "0"  
##      [25,] "1"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [26,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [27,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [28,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [29,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [30,] "1"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [31,] "0"    "0"   "1"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [32,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [33,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [34,] "0"    "0"   "0"     "0"    "1"   "1"      "0"   "0"     "1"  
##      [35,] "1"    "1"   "0"     "1"    "0"   "0"      "0"   "0"     "0"  
##      [36,] "0"    "0"   "0"     "0"    "1"   "0"      "0"   "0"     "0"  
##      [37,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [38,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [39,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [40,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [41,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [42,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [43,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [44,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [45,] "1"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [46,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [47,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [48,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [49,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [50,] "0"    "0"   "1"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [51,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "1"     "0"  
##      [52,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [53,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [54,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [55,] "0"    "1"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [56,] "1"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [57,] "1"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [58,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [59,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [60,] "1"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [61,] "0"    "1"   "0"     "0"    "1"   "0"      "0"   "0"     "0"  
##      [62,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [63,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [64,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [65,] "1"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [66,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [67,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [68,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [69,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [70,] "0"    "0"   "1"     "0"    "0"   "1"      "0"   "0"     "0"  
##      [71,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [72,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "1"  
##      [73,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [74,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [75,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [76,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [77,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [78,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
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##      [81,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
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##      [83,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [84,] "0"    "0"   "0"     "1"    "0"   "0"      "0"   "0"     "0"  
##      [85,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [86,] "0"    "0"   "0"     "0"    "0"   "1"      "0"   "0"     "0"  
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##      [89,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [90,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##      [91,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [92,] "0"    "0"   "0"     "0"    "1"   "0"      "0"   "0"     "0"  
##      [93,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [94,] "1"    "0"   "0"     "0"    "0"   "1"      "0"   "0"     "0"  
##      [95,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [96,] "0"    "0"   "1"     "0"    "0"   "0"      "0"   "0"     "0"  
##      [97,] "1"    "0"   "0"     "0"    "1"   "0"      "0"   "0"     "0"  
##      [98,] "1"    "0"   "0"     "0"    "1"   "1"      "0"   "0"     "0"  
##      [99,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##     [100,] "1"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "0"  
##     [101,] "0"    "0"   "0"     "0"    "0"   "0"      "1"   "0"     "0"  
##     [102,] "0"    "0"   "0"     "0"    "0"   "0"      "0"   "0"     "1"  
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##           Terms
##            status store strateg strategi strong student success support system
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##           Terms
##            take talent target task team technic technolog test thing time tool
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##     [232,] "0"  "1"    "1"    "0"  "1"  "0"     "1"       "0"  "1"   "1"  "0" 
##     [233,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "0"  "0" 
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##     [237,] "1"  "1"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "1"  "0" 
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##     [239,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "1"  "0" 
##     [240,] "0"  "0"    "1"    "0"  "0"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [241,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "1"  "0" 
##     [242,] "0"  "1"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "1"  "0" 
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##     [246,] "0"  "0"    "0"    "0"  "1"  "0"     "1"       "0"  "0"   "1"  "0" 
##     [247,] "0"  "0"    "1"    "0"  "1"  "0"     "1"       "0"  "0"   "0"  "0" 
##     [248,] "0"  "0"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [249,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "1"  "0" 
##     [250,] "0"  "0"    "0"    "0"  "1"  "1"     "1"       "1"  "0"   "0"  "0" 
##     [251,] "1"  "0"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [252,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "1"  "0"   "0"  "0" 
##     [253,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "1"  "0" 
##     [254,] "0"  "1"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [255,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [256,] "0"  "0"    "0"    "1"  "1"  "0"     "1"       "0"  "0"   "0"  "1" 
##     [257,] "0"  "1"    "1"    "0"  "1"  "1"     "1"       "0"  "0"   "0"  "0" 
##     [258,] "0"  "0"    "0"    "0"  "0"  "1"     "0"       "0"  "0"   "0"  "0" 
##     [259,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [260,] "0"  "0"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "1"  "0" 
##     [261,] "0"  "0"    "0"    "0"  "0"  "0"     "1"       "0"  "0"   "1"  "0" 
##     [262,] "0"  "0"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [263,] "0"  "0"    "0"    "0"  "0"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [264,] "0"  "0"    "0"    "0"  "1"  "0"     "0"       "0"  "0"   "0"  "0" 
##     [265,] "0"  "0"    "0"    "0"  "1"  "0"     "0"       "1"  "0"   "1"  "0" 
##           Terms
##            top track train travel understand uniqu unit updat use user valu
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##           Terms
##            various via video want way web websit week well within work world
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##     [222,] "0"     "1" "0"   "0"  "0" "0" "1"    "1"  "0"  "0"    "0"  "1"  
##     [223,] "0"     "0" "0"   "0"  "0" "1" "0"    "0"  "1"  "1"    "1"  "0"  
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##     [225,] "0"     "0" "0"   "0"  "0" "0" "0"    "1"  "1"  "0"    "1"  "0"  
##     [226,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "0"  "1"  
##     [227,] "0"     "0" "0"   "0"  "0" "1" "0"    "0"  "0"  "0"    "0"  "0"  
##     [228,] "0"     "0" "0"   "0"  "0" "1" "0"    "0"  "0"  "0"    "1"  "0"  
##     [229,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "1"    "1"  "0"  
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##     [234,] "0"     "1" "0"   "1"  "0" "0" "0"    "1"  "0"  "0"    "1"  "0"  
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##     [247,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "1"    "1"  "0"  
##     [248,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "1"    "1"  "0"  
##     [249,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "0"  "0"  
##     [250,] "1"     "0" "1"   "1"  "0" "1" "1"    "0"  "0"  "0"    "1"  "1"  
##     [251,] "0"     "0" "0"   "1"  "0" "0" "0"    "0"  "1"  "0"    "0"  "0"  
##     [252,] "0"     "0" "0"   "0"  "1" "0" "0"    "0"  "0"  "0"    "0"  "0"  
##     [253,] "0"     "0" "0"   "0"  "0" "0" "0"    "1"  "0"  "0"    "1"  "0"  
##     [254,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "1"  "0"    "1"  "1"  
##     [255,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "0"  "0"  
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##     [257,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "1"  "0"    "1"  "0"  
##     [258,] "0"     "0" "0"   "1"  "0" "0" "0"    "0"  "0"  "0"    "0"  "0"  
##     [259,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "1"    "1"  "0"  
##     [260,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "1"  "0"  
##     [261,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "0"  "0"  
##     [262,] "0"     "1" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "1"  "0"  
##     [263,] "0"     "0" "0"   "0"  "0" "0" "0"    "0"  "0"  "0"    "0"  "0"  
##     [264,] "0"     "1" "0"   "0"  "0" "0" "0"    "0"  "1"  "0"    "1"  "0"  
##     [265,] "0"     "0" "0"   "0"  "0" "0" "0"    "1"  "0"  "1"    "0"  "0"  
##           Terms
##            write written year
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##      [24,] "0"   "0"     "1" 
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##      [26,] "1"   "0"     "0" 
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##      [28,] "0"   "0"     "1" 
##      [29,] "0"   "0"     "0" 
##      [30,] "0"   "1"     "1" 
##      [31,] "1"   "0"     "0" 
##      [32,] "0"   "0"     "0" 
##      [33,] "0"   "0"     "0" 
##      [34,] "1"   "0"     "0" 
##      [35,] "0"   "0"     "0" 
##      [36,] "0"   "0"     "0" 
##      [37,] "0"   "0"     "1" 
##      [38,] "0"   "0"     "1" 
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##      [43,] "0"   "0"     "1" 
##      [44,] "0"   "0"     "1" 
##      [45,] "1"   "0"     "0" 
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##      [47,] "0"   "0"     "1" 
##      [48,] "1"   "0"     "1" 
##      [49,] "0"   "0"     "0" 
##      [50,] "0"   "0"     "1" 
##      [51,] "0"   "0"     "0" 
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##      [58,] "0"   "0"     "0" 
##      [59,] "0"   "0"     "0" 
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##      [81,] "0"   "0"     "1" 
##      [82,] "0"   "0"     "0" 
##      [83,] "0"   "0"     "0" 
##      [84,] "0"   "0"     "0" 
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##      [86,] "1"   "1"     "0" 
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##      [90,] "0"   "0"     "1" 
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##     [112,] "0"   "1"     "0" 
##     [113,] "0"   "0"     "0" 
##     [114,] "0"   "1"     "1" 
##     [115,] "0"   "0"     "1" 
##     [116,] "0"   "1"     "1" 
##     [117,] "0"   "0"     "0" 
##     [118,] "0"   "1"     "0" 
##     [119,] "0"   "0"     "1" 
##     [120,] "0"   "0"     "0" 
##     [121,] "0"   "0"     "0" 
##     [122,] "0"   "0"     "0" 
##     [123,] "0"   "1"     "0" 
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##     [125,] "0"   "1"     "0" 
##     [126,] "0"   "0"     "0" 
##     [127,] "0"   "0"     "0" 
##     [128,] "1"   "0"     "0" 
##     [129,] "0"   "0"     "0" 
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##     [131,] "0"   "0"     "0" 
##     [132,] "0"   "1"     "0" 
##     [133,] "0"   "0"     "0" 
##     [134,] "0"   "0"     "1" 
##     [135,] "0"   "0"     "0" 
##     [136,] "0"   "0"     "1" 
##     [137,] "0"   "0"     "0" 
##     [138,] "0"   "0"     "0" 
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##     [142,] "0"   "0"     "0" 
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##     [144,] "0"   "0"     "0" 
##     [145,] "0"   "0"     "0" 
##     [146,] "0"   "0"     "0" 
##     [147,] "0"   "1"     "0" 
##     [148,] "0"   "0"     "0" 
##     [149,] "0"   "0"     "0" 
##     [150,] "0"   "0"     "0" 
##     [151,] "0"   "0"     "0" 
##     [152,] "0"   "0"     "0" 
##     [153,] "0"   "0"     "0" 
##     [154,] "0"   "1"     "1" 
##     [155,] "1"   "0"     "0" 
##     [156,] "0"   "0"     "1" 
##     [157,] "0"   "0"     "0" 
##     [158,] "0"   "0"     "0" 
##     [159,] "0"   "0"     "0" 
##     [160,] "0"   "0"     "1" 
##     [161,] "0"   "0"     "0" 
##     [162,] "0"   "0"     "0" 
##     [163,] "0"   "0"     "1" 
##     [164,] "0"   "0"     "0" 
##     [165,] "0"   "0"     "0" 
##     [166,] "0"   "0"     "0" 
##     [167,] "0"   "0"     "0" 
##     [168,] "0"   "0"     "0" 
##     [169,] "0"   "0"     "0" 
##     [170,] "0"   "0"     "0" 
##     [171,] "0"   "0"     "1" 
##     [172,] "0"   "0"     "0" 
##     [173,] "0"   "0"     "0" 
##     [174,] "0"   "0"     "0" 
##     [175,] "0"   "0"     "1" 
##     [176,] "0"   "0"     "0" 
##     [177,] "0"   "0"     "0" 
##     [178,] "0"   "0"     "1" 
##     [179,] "0"   "0"     "0" 
##     [180,] "0"   "0"     "0" 
##     [181,] "0"   "0"     "0" 
##     [182,] "0"   "0"     "0" 
##     [183,] "0"   "0"     "0" 
##     [184,] "0"   "0"     "0" 
##     [185,] "0"   "0"     "0" 
##     [186,] "0"   "0"     "0" 
##     [187,] "0"   "0"     "0" 
##     [188,] "0"   "0"     "0" 
##     [189,] "0"   "0"     "0" 
##     [190,] "0"   "0"     "0" 
##     [191,] "0"   "0"     "0" 
##     [192,] "0"   "0"     "0" 
##     [193,] "0"   "0"     "0" 
##     [194,] "0"   "0"     "0" 
##     [195,] "0"   "0"     "0" 
##     [196,] "0"   "0"     "0" 
##     [197,] "0"   "1"     "1" 
##     [198,] "0"   "1"     "1" 
##     [199,] "0"   "1"     "0" 
##     [200,] "0"   "0"     "0" 
##     [201,] "0"   "0"     "0" 
##     [202,] "0"   "0"     "0" 
##     [203,] "0"   "0"     "0" 
##     [204,] "1"   "0"     "0" 
##     [205,] "0"   "0"     "0" 
##     [206,] "0"   "0"     "1" 
##     [207,] "0"   "0"     "0" 
##     [208,] "0"   "0"     "0" 
##     [209,] "0"   "0"     "0" 
##     [210,] "0"   "0"     "0" 
##     [211,] "0"   "0"     "0" 
##     [212,] "0"   "0"     "0" 
##     [213,] "0"   "0"     "0" 
##     [214,] "0"   "0"     "0" 
##     [215,] "0"   "0"     "0" 
##     [216,] "0"   "0"     "0" 
##     [217,] "0"   "0"     "0" 
##     [218,] "0"   "0"     "1" 
##     [219,] "0"   "0"     "1" 
##     [220,] "0"   "0"     "0" 
##     [221,] "0"   "0"     "0" 
##     [222,] "0"   "0"     "1" 
##     [223,] "0"   "0"     "0" 
##     [224,] "0"   "0"     "0" 
##     [225,] "0"   "0"     "0" 
##     [226,] "0"   "0"     "0" 
##     [227,] "0"   "0"     "0" 
##     [228,] "0"   "0"     "0" 
##     [229,] "0"   "0"     "0" 
##     [230,] "0"   "1"     "0" 
##     [231,] "0"   "0"     "0" 
##     [232,] "0"   "1"     "1" 
##     [233,] "0"   "0"     "0" 
##     [234,] "0"   "0"     "0" 
##     [235,] "0"   "0"     "0" 
##     [236,] "0"   "0"     "0" 
##     [237,] "0"   "0"     "0" 
##     [238,] "0"   "0"     "0" 
##     [239,] "0"   "0"     "0" 
##     [240,] "0"   "0"     "0" 
##     [241,] "0"   "0"     "0" 
##     [242,] "0"   "0"     "1" 
##     [243,] "0"   "0"     "1" 
##     [244,] "0"   "0"     "0" 
##     [245,] "0"   "0"     "0" 
##     [246,] "0"   "0"     "0" 
##     [247,] "0"   "0"     "1" 
##     [248,] "0"   "0"     "1" 
##     [249,] "0"   "0"     "0" 
##     [250,] "0"   "0"     "0" 
##     [251,] "0"   "0"     "0" 
##     [252,] "0"   "0"     "0" 
##     [253,] "1"   "0"     "0" 
##     [254,] "0"   "0"     "1" 
##     [255,] "0"   "0"     "0" 
##     [256,] "0"   "0"     "0" 
##     [257,] "0"   "0"     "0" 
##     [258,] "0"   "0"     "0" 
##     [259,] "0"   "0"     "0" 
##     [260,] "0"   "0"     "0" 
##     [261,] "0"   "0"     "0" 
##     [262,] "1"   "0"     "0" 
##     [263,] "0"   "0"     "0" 
##     [264,] "0"   "0"     "0" 
##     [265,] "0"   "0"     "0" 
##  [ reached getOption("max.print") -- omitted 13145 rows ]

2.10 Modelling with Naive Bayes

We will add laplace smoothing into our model because the element matrix is dominantly 0.

library(e1071)
#model_text <- naiveBayes(x = train_bn, y = train_target, laplace = 1)

There is an error in our model. Based on the error, we can tell that it is because of the imbalance data

2.11 Check proportion of data

prop.table(table(train_target$fraudulent))

## 
##          0          1 
## 0.95205071 0.04794929

prop.table(table(test_target$fraudulent))

## 
##          0          1 
## 0.95011186 0.04988814

Since the data is very imbalanced, we need to either do upsample or downsample. Since our data is a lot, we will do downsample because then the limitations of downsample (the variance of data might be lost) do not apply in this case.

2.12 Downsample

set.seed(100)
splitted <- initial_split(data = job_clean, prop = 0.75, strata = "fraudulent")
train2 <- training(splitted)
test2 <- testing(splitted)

library(caret)
train_down <- downSample(x = train2[,1] , y = train2$fraudulent, yname = "fraudulent")

test_down <- downSample(x = test2[,1] , y = test2$fraudulent, yname = "fraudulent")

train.corpus <- VCorpus(VectorSource(train_down$description))
test.corpus <- VCorpus(VectorSource(test_down$description))

train.corpus <- tm_map(train.corpus, FUN = removeNumbers)
train.corpus <- tm_map(train.corpus, removeWords, stopwords("english"))
train_down.corpus <- tm_map(train.corpus, removePunctuation)

test.corpus <- tm_map(test.corpus, FUN = removeNumbers)
test.corpus <- tm_map(test.corpus, removeWords, stopwords("english"))
test.corpus <- tm_map(test.corpus, removePunctuation)

library(SnowballC)
train.corpus <- tm_map(train.corpus, stemDocument)
test.corpus <- tm_map(test.corpus, stemDocument)

train.corpus <- tm_map(train.corpus, stripWhitespace)
test.corpus <- tm_map(test.corpus, stripWhitespace)

train_dtm <- DocumentTermMatrix(train.corpus)
test_dtm <- DocumentTermMatrix(test.corpus)

train_freq2 <- findFreqTerms(train_dtm, 50)
test_freq2 <- findFreqTerms(test_dtm, 50)
length(train_freq2)

## [1] 511

length(test_freq2)

## [1] 155

train_freq00 <- train_dtm[, train_freq2]
test_freq00 <- test_dtm[,test_freq2]

train_bn00 <- apply(train_freq00, 2, bernoulli_conv)
test_bn00 <- apply(test_freq00, 2, bernoulli_conv)

model_text <- naiveBayes(x = train_bn00, y = train_down$fraudulent, laplace = 1)

prediction <- predict(model_text, newdata = test_bn00)

2.13 Model evaluation

2.13.1 Confusion Matrix

confusionMatrix(data = prediction, reference = test_down$fraudulent, positive = "1")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 146  41
##          1  47 152
##                                           
##                Accuracy : 0.772           
##                  95% CI : (0.7269, 0.8129)
##     No Information Rate : 0.5             
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.544           
##                                           
##  Mcnemar's Test P-Value : 0.594           
##                                           
##             Sensitivity : 0.7876          
##             Specificity : 0.7565          
##          Pos Pred Value : 0.7638          
##          Neg Pred Value : 0.7807          
##              Prevalence : 0.5000          
##          Detection Rate : 0.3938          
##    Detection Prevalence : 0.5155          
##       Balanced Accuracy : 0.7720          
##                                           
##        'Positive' Class : 1               
## 

In this case, we will prioritise the recall metric over precision because it is worse to predict a fake job listing as a real one (applicants can get scammed) rather than predicting a real job listing as fake (at most applicants just won't apply for that job listing and it is not so grave as there are other job listings available).

From this confusion matrix, the recall is 0.7349 whereas the accuracy is 0.7269 and the precision is 0.7233. I would say this model fares quite well (>70% in all 3 metrics)

2.13.2 ROC/AUC

library(ROCR)
prob <- predict(model_text, newdata = test_bn00, type = "raw")
head(prob)

##                 0            1
## [1,] 0.7997184801 0.2002815199
## [2,] 0.9603832807 0.0396167193
## [3,] 0.0408354471 0.9591645529
## [4,] 0.0004261342 0.9995738658
## [5,] 0.4949285337 0.5050714663
## [6,] 0.9995828128 0.0004171872

pred_roc <- prediction(predictions = prob[,1],
           labels = as.numeric(ifelse(test_down$fraudulent == "1", "1", "0")))

roc <- performance(pred_roc, measure = "tpr", x.measure = "fpr")

plot(roc)

auc <- performance(pred_roc, "auc")
auc@y.values

## [[1]]
## [1] 0.1785014

Based on the plot of ROC as well as AUC, this model fares very badly. This is because the value of AUC is very far from 1. This means that the probability that the model ranks a random positive example more highly than a random negative example is very very low (20%).

2.14 Conclusion

Although this model fares well in terms of confusion matrix, it fares badly in terms of ROC and AUC. This may be possible because confusion matrix only measures the metrics at a given threshold (in this case 0.5), whereas ROC measures it at every threshold possible. Regardless of it, this model either needs improvement or this dataset is just not suitable for naive bayes modelling.